TrialMind Workflow: TLF Mockup and Derivation for Clinical Trials

TrialMind Workflow: ADaM Table Derivation for Clinical Trials

TrialMind Workflow: Patient Extraction from Real-World Database

TrialMind Workflow: Patient-to-Trial Matching (TrialGPT)

Biomedical Data Science

Systematic literature review with TrialMind

Eligibility criteria optimization and feasibility analysis from real-world data

How Generative AI system TrialMind Saves Hours for Biostatisticians & Biomedical Researchers

TrialMind for PTRS Assessment

PyHealth Casual Chats 7 - Reflection on Deep Learning for Healthcare

PyHealth Casual Chats 6 - More Memory Streaming Updates

PyHealth Casual Chats 5 - Discussion around Memory Streaming

Data science agent for reproducing biomedical research

Systematic Literature Review demo

PyHealth Casual Chats 4 - Discussion around EHR, codes, and modeling

PyHealth Casual Chats 3 - Memory Efficiency & Future Roadmap

PyHealth Casual Chats 2 - Interpretability Module

PyHealth Casual Chats 1 - Interpretable Patient Journey Research Project

#27 Connor Coley, Tailoring generative AI to small molecule design for early stage (drug) discovery

#26 Edward Choi, Interactive Evaluation of AI’s Multi-modal Medical Reasoning Capabilities

Generative AI for Biomedical Applications: Images, Molecules, Wearables, and Conversations

Keiji AI's TrialMind Systematic Literature Review Agent

AI for Health #24 Making Neural Nets More Causally Accurate for Clinical Applications, David Page

Model Context Protocol"(MCP) for health video

AI4H #23, David Sontag, Towards Precision Oncology: A Predictive, Causal, and LLM Lens

AI4H #22, Hua Xu, Large Language Models for Biomedical Applications

AI4H #20, Tristan Naumann, Biomedical NLP in the Age of GenAI

AI4H #14, Faisal Mahmood, Multimodal Generative and Agentic AI for Pathology

AI4H #21, Aaron Lee, Building biomedical datasets for the future of AI

A4H#11: Jenna Wiens, White Coat, Black Box -Navigating Pitfalls in AI Diagnostic Decision Making

AI4H #17, Sunlab team, PyHealth Overview and Discussion

SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation

AI4H #19, Emily Alsentzer, Knowledge-Enhanced ML for Healthcare

AI4H#15 Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development

AI4H #16, Sanmi Koyejo Enhancing Temporal Processing in EHR Foundation Models by Prof. Sanmi Koyejo

AI4H #13, Zifeng Wang, Large language models for clinical trial design, execution, and analysis

Matching patients to clinical trials with large language models

Biomedical literature mining with AI

AI4H #12, James Zou, AI agents for biomedicine and healthcare

AI4H #10, Sheng Wang, Generative AI for Multimodal Biomedicine in pathology and retinal imaging

AI4H #9, Marzyeh Ghassemi, Taking the Pulse Of Ethical ML in Health

TrialMind HLTH'24 demo

AI4H #7, Bradley Malin, Baking Responsibility into the AI for Health Lifecycle

AI4H #6: Pranav Rajpurkar, Harvard University, The Generalist Medical AI Will See You Now,

A4H #5: Hoifung Poon, Multimodal Generative AI for Precision Health

AI4H #1, Vera Mucaj, AI and the healthcare data logistics problem by Vera Mucaj

A4H #4: Parminder Bhatia, Unleashing the power of Medical Imaging Foundation Models

Panacea - finetune a small LLM for clinical trial tasks

Automated cancer Survivorship Care Plan (SCP) generation with large language models

Medisyn demo - Clinical predictive modeling with synthetic data augmentation

(Q&A) AI Maturity - how to maximize the impact of AI via a disciplined approach

AI Maturity: how to maximize the impact of AI via a disciplined approach

Computer Science for Neurodegenerative disorders

Using ML to Make Cell Therapy Safer & More Accessible

PyHealth Live - 09 Support Medical Imaging Data

(Q&A) MLOps – Sustaining Data Science in Production

MLOps – Sustaining Data Science in Production

PyHealth Live - 08 (Part II) Demo: Build Biosignal Pipeline in PyHealth

PyHealth Live - 08 (Part I) Support Biosignals Inputs (e.g., EEG, ECG) with the Same APIs

PyHealth Live - 07 Adopt Customized Model into Pyhealth